Massive MIMO-FDD self-attention CSI feedback network for outdoor environments

In situations where traditional feedback methods face high complexity and rely on channel sparsity, many deep learning-based CSI compression feedback methods have shown the potential to fully capture the diversity and multiplexing gains of Massive MIMO technology in Frequency Division Duplex (FDD) mode. In order to further enhance the accuracy of obtaining downlink channel state information at the Base Station (BS) side, this paper proposes a novel neural network, SfNet. Based on visual analysis, the convolutional layers in the encoder are designed to extract long-distance time-delay correlations of the same antenna across different subcarriers. The SimAM module is introduced to mitigate clustering effects. In the decoder, a spatial-temporal joint modeling approach is presented, utilizing the Convolutional Block Attention Module (CBAM) module to preserve the original spatial structure features lost during the dimension reduction of the CSI matrix to sequence data. Subsequently, two layers of Transformer multi-head attention are employed to achieve more global time-series modeling.The experimental results indicate that, compared to CLNet, the average complexity of SfNet’s encoder is reduced by 3.4%. It meets the demand for lightweight devices on the user side. In terms of accuracy, SfNet exhibits an average improvement of 30.17% compared to TransNet in outdoor environments.